ClinicalTrials.gg
CompletedNCT07712198Updated Sep 24, 2026

Al Prediction of Sarcopenia Risk in Neurocritical ICU Patients

An observational study in Intracerebral Hemorrhage, Subarachnoid Hemorrhage and Subdural Hematoma, sponsored by Trabzon Kanuni Education and Research Hospital. Completed at 1 site in Turkey (Türkiye). Open to participants aged 18 Years to 65 Years. Per ClinicalTrials.gov, last updated 2026-09-24.

Sponsored by Trabzon Kanuni Education and Research Hospital · Observational

Study type
Observational
Model
Cohort
Time perspective
Prospective
Enrollment
72
Ages
18 Years to 65 Years
Sex
All
01

Study summary

This prospective observational study aims to evaluate sarcopenia in intensive care patients with intracranial pathologies using ultrasound and to compare the predictive performance of different artificial intelligence models. Rectus femoris muscle thickness will be measured by ultrasound on ICU admission (Day 0) and Day 7. Prealbumin levels will be assessed on Days 0, 3, and 7, and the modified Nutrition Risk in Critically Ill (mNUTRIC) score will be calculated on the first day of ICU admission. Clinical, laboratory, and ultrasonographic data will be integrated into different artificial intelligence models to predict sarcopenia status on Day 7. The study aims to determine the effectiveness of artificial intelligence in the early identification of sarcopenia and to support future clinical decision-making in intensive care practice.

Read the detailed description

This study is designed as a prospective observational study. Patients admitted to the Level III Intensive Care Units of Trabzon University Faculty of Medicine, Kanuni Training and Research Hospital, Kaşüstü Campus, due to intracranial pathologies between January 1, 2026, and June 30, 2026, will be included. Approximately 100-150 patients are planned to be evaluated.

Demographic data of the enrolled patients will be recorded, and the modified Nutrition Risk in Critically Ill (mNUTRIC) score will be calculated on the first day of intensive care unit admission. Rectus femoris muscle thickness will be evaluated by ultrasonography on Day 0 and Day 7 of ICU admission. All ultrasonographic measurements will be performed using the same ultrasound device and by the same investigator according to a standardized protocol. During the measurements, the patient will be positioned supine, the knee will be kept in extension, and the muscle will be evaluated in a relaxed position. Three repeated measurements will be obtained at each assessment, and the mean value will be recorded.

As part of the laboratory assessment, prealbumin levels will be measured on Days 0, 3, and 7. Biochemical parameters evaluated during routine clinical follow-up will be recorded from the hospital information system.

No intervention, additional procedure, or treatment modification will be performed as part of this study. All data will consist of observational data obtained during routine clinical follow-up. Data collection will be conducted by a resident physician from the Department of Anesthesiology and Reanimation with experience in intensive care.

The collected clinical, laboratory, and ultrasonographic data will be provided to different artificial intelligence models, and their accuracy and performance in predicting sarcopenia development on Day 7 will be evaluated. The primary objective of the study is to assess the predictive performance of artificial intelligence models, including ChatGPT, Gemini, and Claude, for Day 7 sarcopenia development in intensive care patients with intracranial pathologies. Secondary objectives include comparing artificial intelligence predictions with clinical assessments, comparing predictive performance among different artificial intelligence models, and evaluating the potential usability of artificial intelligence models as clinical decision-support tools in intensive care practice.

All data will be de-identified before analysis, and patient confidentiality will be maintained. Study data will be stored in a secure digital environment accessible only to the research team.

02

Conditions studied

  • Intracerebral Hemorrhage
  • Subarachnoid Hemorrhage
  • Subdural Hematoma
  • Epidural Hematoma
  • Ischemic Stroke
  • Brain Neoplasms

Keywords

  • Sarcopenia
  • Artificial Intelligence
  • Intensive Care Unit
  • Rectus Femoris
  • Ultrasonography
  • mNUTRIC
  • Prealbumin
  • Intracranial Pathology
03

In context

Cerebral Hemorrhage

457 studies on the registry are indexed under Cerebral Hemorrhage; 172 are open to participants now.

This study's enrollment of 72 is below the median of 300 across 180 observational studies indexed under Cerebral Hemorrhage.

Browse Cerebral Hemorrhage studies →

Lead sponsor

Trabzon Kanuni Education and Research Hospital is the lead sponsor of 12 studies on the registry; 4 are open to participants now.

Counted across the registry records on this site, refreshed daily.

04

Who can participate

Ages eligible
18 Years to 65 Years
Sexes eligible
All
Accepts healthy volunteers
No
Sampling method
Non-probability sample

Study population

Adult intensive care unit patients aged 18-65 years admitted with intracranial pathologies, including intracerebral hemorrhage, epidural hemorrhage, subdural hemorrhage, subarachnoid hemorrhage, intracranial tumors, and ischemic stroke.

Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.

Inclusion criteria

  • Age between 18 and 65 years
  • Admission to the intensive care unit due to intracranial pathology (intracerebral hemorrhage, epidural hemorrhage, subdural hemorrhage, subarachnoid hemorrhage, intracranial tumors, or ischemic stroke)
  • obtained from the patient or legally authorized representative

Exclusion criteria

Exclusion Criteria:

  • Age \<18 years or >65 years
  • Failure to achieve nutritional targets according to ESPEN guidelines
  • Palliative care or home care patients
  • Morbid obesity ( ≥40 kg/m²)
  • History of neuromuscular disease
  • Lower extremity amputation
  • History of trauma affecting the thigh region
  • Pregnancy
Terms in these criteria, in plain language(2)

Underlined terms in the criteria open these too. Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.

Informed consent

Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.

You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.

BMI (body mass index)

Weight in kilograms divided by height in metres squared. Example: 70 kg and 1.75 m gives 70 ÷ (1.75 × 1.75) ≈ 22.9.

What counts as overweight or obesity differs between countries (some use lower cut-offs), so go by the range the study states.

05

Study design

Observational model
Cohort
Time perspective
Prospective
Enrollment
72 participants (actual)
Patient registry
No

Groups and cohorts

  • Intracranial Pathology ICU Patients

    Adult patients admitted to the intensive care unit with intracranial pathologies, including intracerebral hemorrhage, subarachnoid hemorrhage, subdural hematoma, epidural hematoma, intracranial tumors, and ischemic stroke. Participants will be prospectively observed. Rectus femoris muscle thickness will be measured by ultrasonography on days 0 and 7, and prealbumin levels will be assessed on days 0, 3, and 7. No experimental intervention or treatment modification will be performed.

    Other: Prospective Observational Assessment

Interventions

  • OtherProspective Observational Assessment

    Prospective observational assessment including rectus femoris ultrasonography, prealbumin measurements, mNUTRIC scoring, and collection of routine clinical data. No experimental intervention or treatment modification will be performed.

06

What researchers measure

Primary outcomes

  1. Accuracy of Artificial Intelligence Models in Predicting Day-7 Sarcopenia

    Evaluation of the predictive performance of ChatGPT, Gemini, and Claude models for day-7 sarcopenia in ICU patients with intracranial pathology using rectus femoris muscle thickness, prealbumin levels, and clinical data.

    Time frame: 7 Days

Secondary outcomes

  1. Comparison of Predictive Performance Among AI Models

    Comparison of prediction accuracy among ChatGPT, Gemini, and Claude models for day-7 sarcopenia.

    Time frame: 7 Days

  2. Agreement Between AI Predictions and Clinical Assessment

    Evaluation of concordance between artificial intelligence model predictions and clinically determined sarcopenia status.

    Time frame: 7 Days

07

Study locations

1 site
  • Trabzon University Faculty of Medicine, Kanuni Training and Research Hospital, Trabzon, 61080
    Trabzon, Trabzon, Turkey (Türkiye)
08

References and documents

Publications

  • Phongpreecha T, Ghanem M, Reiss JD, Oskotsky TT, Mataraso SJ, De Francesco D, Reincke SM, Espinosa C, Chung P, Ng T, Costello JM, Sequoia JA, Razdan S, Xie F, Berson E, Kim Y, Seong D, Szeto MY, Myers F, Gu H, Feister J, Verscaj CP, Rose LA, Sin LWY, Oskotsky B, Roger J, Shu CH, Shome S, Yang LK, Tan Y, Levitte S, Wong RJ, Gaudilliere B, Angst MS, Montine TJ, Kerner JA, Keller RL, Shaw GM, Sylvester KG, Fuerch J, Chock V, Gaskari S, Stevenson DK, Sirota M, Prince LS, Aghaeepour N. AI-guided precision parenteral nutrition for neonatal intensive care units. Nat Med. 2025 Jun;31(6):1882-1894. doi: 10.1038/s41591-025-03601-1. Epub 2025 Mar 25. PubMed 40133525 ↗
  • Lopez-Gomez JJ, Sanchez-Lite I, Fernandez-Velasco P, Izaola-Jauregui O, Cebria A, Perez-Lopez P, Gonzalez-Gutierrez J, Estevez-Asensio L, Primo-Martin D, Gomez-Hoyos E, Jorge-Godoy E, De Luis-Roman DA. Artificial intelligence-assisted rectus femoris ultrasound vs. L3 computed tomography for sarcopenia assessment in oncology patients: establishing diagnostic cut-offs for muscle mass and quality. Front Nutr. 2025 Sep 25;12:1678989. doi: 10.3389/fnut.2025.1678989. eCollection 2025. PubMed 41080186 ↗
  • Choi YH, Kim DH, Jeon ET, Lee HJ, Park TY, Yoon SH, Jin KN, Lee HW. Cluster analysis of thoracic muscle mass using artificial intelligence in severe pneumonia. Sci Rep. 2024 Jul 23;14(1):16912. doi: 10.1038/s41598-024-67625-2. PubMed 39043882 ↗

Individual participant data

Plan to share: No — Individual participant data will not be shared. The collected data will be used only for the purposes of this study and will remain confidential in accordance with institutional and ethical regulations.

09

Updates

Tracking since Sep 25, 2026
No changes since tracking began. The registry record was last updated on Sep 24, 2026, before this site started recording changes on Sep 25, 2026. Its history is on ClinicalTrials.gov ↗
10

Registry details

Key details

Study ID
NCT07712198
Lead sponsor
Trabzon Kanuni Education and Research Hospital
Responsible party
Sponsor
First posted
Jul 17, 2026
Start date
Jan 1, 2026
Primary completion
Jun 30, 2026
Completion
Jun 30, 2026
Last update
Sep 24, 2026

Oversight

Data monitoring committee
No
FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

Not currently enrolling

This study is completed, as verified in Aug 2026. You cannot join it, but the record below documents what was studied.

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